In this article, we propose a nonlinear receding horizon game-theoretic planner for
autonomous cars in competitive scenarios with other cars. The online planner is specifically …

We present a new method for multi-agent planning involving human drivers and
autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging …

Existing game-theoretic planning methods assume that the robot knows the objective
functions of the other agents a priori while, in practical scenarios, this is rarely the case. This …

We present the concept of a generalized feedback Nash equilibrium (GFNE) in dynamic
games, extending the feedback Nash equilibrium concept to games in which players are …

Information gathering while interacting with other agents under sensing and motion
uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The …

In this work, we develop a scalable, local tra-jectory optimization algorithm that enables
robots to interact with other robots. It has been shown that agents' interactions can be …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications as …

Robots and autonomous systems must interact with one another and their environment to
provide high-quality services to their users. Dynamic game theory provides an expressive …

We focus on decentralized navigation among multiple non-communicating rational agents at
{\em uncontrolled} intersections, ie, street intersections without traffic signs or signals …

We describe Urban Driving Games (UDGs) as a particular class of differential games that
model the interactions and incentives of the urban driving task. The drivers possess a …